{"id":"W2533383645","doi":"10.1002/smr.1819","title":"Error leakage and wasted time: sensitivity and effort analysis of a requirements consistency checking process","year":2016,"lang":"en","type":"article","venue":"Journal of Software Evolution and Process","topic":"Software Engineering Research","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"National Science Foundation","keywords":"Consistency (knowledge bases); Computer science; Reliability engineering; Process (computing); Sequential consistency; Model checking; Consistency model; Data mining; Data consistency; Distributed computing; Engineering; Theoretical computer science; Programming language; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01997735,0.0009209882,0.0008592595,0.003974219,0.0006221279,0.002294863,0.001356238,0.00131643,0.001283979],"category_scores_gemma":[0.1195382,0.0007170827,0.001383202,0.002519941,0.002018561,0.002762468,0.002003712,0.001750223,0.0001174571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003418117,"about_ca_system_score_gemma":0.001468397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005773846,"about_ca_topic_score_gemma":0.002324277,"domain_scores_codex":[0.9756113,0.01465303,0.001043133,0.001815906,0.005866218,0.001010456],"domain_scores_gemma":[0.7015618,0.2592272,0.01403641,0.01487824,0.009272961,0.001023488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0009458681,0.0005480555,0.04111399,0.0002312593,0.0003711934,0.0004177798,0.001056435,0.9107494,0.008701149,0.01197514,0.0002853997,0.02360429],"study_design_scores_gemma":[0.00002435315,0.0003155296,0.00734467,0.00003025656,0.00009499747,0.0000987711,0.0001921069,0.9808738,0.005771301,0.004989055,0.0002220621,0.00004322985],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9177408,0.00021068,0.07844369,0.0003646847,0.00001653318,0.0001921286,0.0001995371,0.0003617222,0.002470259],"genre_scores_gemma":[0.9909382,0.00002523055,0.008778078,0.00002427231,0.000003647545,0.00003946493,0.0000522359,0.00001949403,0.0001194273],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01997735,"threshold_uncertainty_score":0.1056516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0188559898882432,"score_gpt":0.2918275587189101,"score_spread":0.2729715688306669,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}